Text prediction using combined word N-gram and unigram language models

ABSTRACT

Systems and processes are disclosed for predicting words in a text entry environment. Candidate words and probabilities associated therewith can be determined by combining a word n-gram language model and a unigram language model. Using the word n-gram language model, based on previously entered words, candidate words can be identified and a probability can be calculated for each candidate word. Using the unigram language model, based on a character entered for a new word, candidate words beginning with the character can be identified along with a probability for each candidate word. In some examples, a geometry score can be included in the unigram probability related to typing geometry on a virtual keyboard. The probabilities of the n-gram language model and unigram model can be combined, and the candidate word or words having the highest probability can be displayed for a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Ser. No.62/005,942, filed on May 30, 2014, entitled TEXT PREDICTION USINGCOMBINED WORD N-GRAM AND UNIGRAM LANGUAGE MODELS, which is herebyincorporated by reference in its entirety for all purposes.

This application also relates to the following provisional applications:U.S. Patent Application Ser. No. 62/005,837, “Device, Method, andGraphical User Interface for a Predictive Keyboard,” filed May 30, 2014;U.S. Patent Application Ser. No. 62/046,876, “Device, Method, andGraphical User Interface for a Predictive Keyboard,” filed Sep. 5, 2014;U.S. Patent Application Ser. No. 62/006,036, “Entropy-Guided TextPrediction Using Combined Word and Character N-gram Language Models,”filed May 30, 2014; U.S. Patent Application Ser. No. 62/006,010,“Predictive Text Input,” filed May 30, 2014; and U.S. Patent ApplicationSer. No. 62/005,958, “Canned Answers in Messages,” filed May 30, 2014;which are hereby incorporated by reference in their entirety.

FIELD

This relates generally to text prediction and, more specifically, topredicting words by combining word n-gram and unigram language models.

BACKGROUND

Electronic devices and the ways in which users interact with them areevolving rapidly. Changes in size, shape, input mechanisms, feedbackmechanisms, functionality, and the like have introduced new challengesand opportunities relating to how a user enters information, such astext. Statistical language modeling can play a central role in many textprediction and recognition problems, such as speech or handwritingrecognition and keyboard input prediction. An effective language modelcan be critical to constrain the underlying pattern analysis, guide thesearch through various (partial) text hypotheses, and/or contribute tothe determination of the final outcome. In some examples, statisticallanguage modeling has been used to convey the probability of occurrencein the language of all possible strings of n words.

Given a vocabulary of interest for the expected domain of use,determining the probability of occurrence of all possible strings of nwords has been done using a word n-gram model, which can be trained toprovide the probability of the current word given the n−1 previouswords. Training has typically involved large machine-readable textdatabases, comprising representative documents in the expected domain.It can, however, be impractical to enumerate the entire contents of aword n-gram model or dictionary after every keystroke. In addition,n-gram models can fail to account for character-by-character changes asa user enters new information, and can thus fail to provide reliableresults in some circumstances.

Unigram language models have similarly been used in text prediction andlike applications. Unigram language models can produce a probability ofa word in a target language. In some examples, unigram language modelscan accept a prefix of a word (e.g., a character) and produce candidatewords beginning with that prefix along with probabilities associatedwith the candidate words. Unigram language models, however, can fail toaccount for previously entered words or other context, and can thus failto provide reliable results in some circumstances.

Accordingly, using either a word n-gram model or a unigram model fortext prediction can limit overall prediction accuracy and reliability.

SUMMARY

Systems and processes are disclosed for predicting words. In oneexample, typed input can be received from a user. The typed input caninclude a character associated with a new word. Using a word n-grammodel, a first probability of a predicted word can be determined basedon a previously entered word in the typed input. Using a unigram model,a second probability of the predicted word can be determined based onthe character associated with the new word in the typed input. Acombined probability of the predicted word can be determined based onthe first probability and the second probability. The predicted word canbe displayed based on the combined probability.

In some examples, in determining the first probability, a set ofpredicted words and associated probabilities can be determined based onthe previously entered word in the typed input. A subset of the set ofpredicted words can be generated by removing words from the set based onthe character associated with the new word in the typed input. Thesubset of predicted words can include words having prefixes that includethe character associated with the new word in the typed input. Thesubset of predicted words can include the predicted word.

In other examples, the second probability can be determined based on ageometry score associated with the typed input. The geometry score canbe determined based on a key selection. The geometry score can include alikelihood of a sequence of characters given the key selection. The keyselection can include key selection on a virtual keyboard.

In some examples, in determining the second probability, a set ofpredicted words and associated probabilities can be determined based onthe character associated with the new word in the typed input. The setof predicted words can include words having prefixes that include thecharacter associated with the new word in the typed input. The set ofpredicted words can include the predicted word.

In other examples, the second probability can be determined bytraversing a unigram tried to determine the second probability. Thefirst probability can be determined based on a sequence of previouslyentered words. The second probability can be determined based on asequence of characters in the typed input associated with the new word.The combined probability can be determined using the product of thefirst probability and the second probability.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for predicting words.

FIG. 2 illustrates an exemplary process for predicting words using aword n-gram model and a unigram model.

FIG. 3 illustrates exemplary n-gram and unigram model predictions.

FIG. 4 illustrates text entry using an exemplary virtual keyboard.

FIG. 5 illustrates a functional block diagram of an electronic deviceconfigured to predict words using a word n-gram model and a unigrammodel.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings in which it is shown by way of illustrationspecific examples that can be practiced. It is to be understood thatother examples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

This relates to systems and processes for predicting words in a textentry environment. In one example, candidate words and probabilitiesassociated therewith can be determined by combining a word n-gramlanguage model and a unigram language model. Using the word n-gramlanguage model, based on previously entered words, candidate words canbe identified and a probability can be calculated for each candidateword. Such an n-gram probability can signify a likelihood that acandidate word corresponds to a word a user will enter. Using theunigram language model, based on one or more characters entered for anew word, candidate words beginning with the one or more enteredcharacters can similarly be identified along with a probability for eachcandidate word. Such a unigram probability can, for example, signify alikelihood of a candidate word based on usage frequency in a particularlanguage. The probabilities of the n-gram language model and unigrammodel can be combined, and the candidate word or words having thehighest probability can be displayed for a user.

In some examples, the combined probability can take into accountgeometry information related, for example, to typing on a virtualkeyboard. For example, users typing on a virtual keyboard (e.g., on atouchscreen) can make ambiguous key selections (e.g., based on small keysize, imprecise typing, finger size, etc.). The geometry of a user's keyselections can be considered in determining word likelihood bybroadening the analysis to account for nearby keys, inverted typingmistakes, misspelled words, and the like. In one example, a geometryscore can be introduced into the unigram probability. In other examples,the unigram probability can be determined based on geometry (e.g.,typing location) instead of a specific character or characters. In stillother examples, various techniques for auto-correction in virtualkeyboard typing can be used to address imprecision when predicting wordsaccording to the examples herein.

By combining a word n-gram language model and unigram model, thestrengths of each approach can be leveraged at the same time. Forexample, given little character guidance of a new word, a word n-grammodel can provide likely word candidates based on linguistic context. Onthe other hand, given significant prefix characters for a new word, aunigram model can provide likely word candidates matching the knownprefix. Combining the two approaches can aid in providing accurate andmeaningful candidate word suggestions or corrections to a user enteringtext. With meaningful candidate word suggestions or corrections, a usercan enter text quickly and efficiently by selecting suggested candidatesinstead of entering all characters individually for all words or byallowing typing mistakes to be automatically corrected according to theword prediction approaches discussed herein. It should be understood,however, that still many other advantages can be achieved according tothe various examples discussed herein.

FIG. 1 illustrates exemplary system 100 for predicting words. In oneexample, system 100 can include user device 102 (or multiple userdevices 102) that can provide a text entry interface or environment.User device 102 can include any of a variety of devices, such as acellular telephone (e.g., smartphone), tablet computer, laptop computer,desktop computer, portable media player, wearable digital device (e.g.,digital glasses, wristband, wristwatch, brooch, armbands, etc.),television, set top box (e.g., cable box, video player, video streamingdevice, etc.), gaming system, or the like. In some examples, user device102 can include display 114. Display 114 can include any of a variety ofdisplays, and can also include a touchscreen, buttons, or otherinteractive elements. In one example, display 114 can be incorporatedwithin user device 102 (e.g., as in a touchscreen, integrated display,etc.). In other examples, display 114 can be external to—butcommunicatively coupled to—user device 102 (e.g., as in a television,external monitor, projector, etc.).

In some examples, user device 102 can include or be communicativelycoupled to keyboard 116, which can capture user-entered text (e.g.,characters, words, symbols, etc.). Keyboard 116 can include any of avariety of text entry mechanisms and devices, such as a stand-aloneexternal keyboard, a virtual keyboard, a remote control keyboard, ahandwriting recognition system, or the like. In one example, forinstance, keyboard 116 can include a virtual keyboard on a touchscreencapable of receiving text entry from a user (e.g., detecting characterselections from touch). In another example, keyboard 116 can include avirtual keyboard shown on a display (e.g., display 114), and a pointeror other indicator can be used to indicate character selection (e.g.,indicating character selection using a mouse, remote control, pointer,button, gesture, eye tracker, etc.). In yet another example, keyboard116 can include a touch sensitive device capable of recognizinghandwritten characters. In still other examples, keyboard 116 caninclude other mechanisms and devices capable of receiving text entryfrom a user.

User device 102 can also include processor 104, which can receive textentry from a user (e.g., from keyboard 116) and interact with otherelements of user device 102 as shown. In one example, processor 104 canbe configured to perform any of the methods discussed herein, such aspredicting words and causing them to be displayed by combining a wordn-gram language model and a unigram model. In other examples, processor104 can cause data (e.g., entered text, user data, etc.) to betransmitted to server system 120 through network 118. Network 118 caninclude any of a variety of networks, such as a cellular telephonenetwork, WiFi network, wide area network, local area network, theInternet, or the like. Server system 120 can include a server, storagedevices, databases, and the like and can be used in conjunction withprocessor 104 to perform any of the methods discussed herein. Forexample, processor 104 can cause an interface to be provided to a userfor text entry, can receive entered information, can transmit some orall of the entered information to server system 120, and can causepredicted words to be displayed on display 114.

In some examples, user device 102 can include storage device 106, memory108, word n-gram language model 110, and unigram model 112. In someexamples, word n-gram language model 110 and unigram model 112 can bestored on storage device 106, and can be used to predict words anddetermine probabilities according to the methods discussed herein.Language models 110 and 112 can be trained on any of a variety of textdata, and can include domain-specific models for use in particularapplications, as will be appreciated by one of ordinary skill in theart.

Thus, any of the functions or methods discussed herein can be performedby a system similar or identical to system 100. It should be appreciatedthat system 100 can include instructions stored in a non-transitorycomputer-readable storage medium, such as memory 108 or storage device106, and executed by processor 104. The instructions can also be storedand/or transported within any non-transitory computer-readable storagemedium for use by or in connection with an instruction execution system,apparatus, or device, such as a computer-based system,processor-containing system, or other system that can fetch theinstructions from the instruction execution system, apparatus, or deviceand execute the instructions. In the context of this document, a“non-transitory computer-readable storage medium” can be any medium thatcan contain or store the program for use by or in connection with theinstruction execution system, apparatus, or device. The non-transitorycomputer-readable storage medium can include, but is not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, a portable computer diskette(magnetic), a random access memory (RAM) (magnetic), a read-only memory(ROM) (magnetic), an erasable programmable read-only memory (EPROM)(magnetic), a portable optical disc such as CD, CD-R, CD-RW, DVD, DVD-R,or DVD-RW, or flash memory such as compact flash cards, secured digitalcards, USB memory devices, memory sticks, and the like.

The instructions can also be propagated within any transport medium foruse by or in connection with an instruction execution system, apparatus,or device, such as a computer-based system, processor-containing system,or other system that can fetch the instructions from the instructionexecution system, apparatus, or device and execute the instructions. Inthe context of this document, a “transport medium” can be any mediumthat can communicate, propagate, or transport the program for use by orin connection with the instruction execution system, apparatus, ordevice. The transport medium can include, but is not limited to, anelectronic, magnetic, optical, electromagnetic, or infrared wired orwireless propagation medium.

It should be understood that the system is not limited to the componentsand configuration of FIG. 1, but can include other or additionalcomponents in multiple configurations according to various examples. Forexample, user device 102 can include a variety of other mechanisms forreceiving input from a user, such as a microphone, optical sensor,camera, gesture recognition sensor, proximity sensor, ambient lightsensor, or the like. Additionally, the components of system 100 can beincluded within a single device, or can be distributed among multipledevices. For example, although FIG. 1 illustrates word n-gram languagemodel 110 and unigram model 112 as part of user device 102, it should beappreciated that, in other examples, the functions of processor 104 canbe performed by server system 120, and/or word n-gram language model 110and unigram model 112 can be stored remotely as part of server system120 (e.g., in a remote storage device). In still other examples,language models and other data can be distributed across multiplestorage devices, and many other variations of system 100 are alsopossible.

FIG. 2 illustrates exemplary process 200 for predicting words using aword n-gram model and a unigram model. Process 200 can, for example, beexecuted on processor 104 of system 100 utilizing word n-gram languagemodel 110 and unigram model 112 discussed above with reference toFIG. 1. At block 202, typed input can be received from a user. Typedinput can be received in any of a variety of ways, such as from keyboard116 in system 100 discussed above. In one example, the typed input caninclude a character associated with a new word (e.g., a letter enteredfor a new word following a space or other break). The typed input canalso include symbols, a string of characters, a word, multiple words,multiple sentences, or the like. User-entered input received at block202 can be directed to any type of text entry interface or environmenton a user device. For example, such an interface could be configured fortyping text messages, emails, web addresses, documents, presentations,search queries, media selections, commands, form data, calendar entries,notes, or the like.

The typed input received at block 202 can be used to predict a word. Forexample, the typed input can be used to predict the likely completion ofa partially-entered word, a subsequent word likely to be enteredfollowing previously-entered words, a phrase or a group of words likelyto be entered following previously-entered words, or the like.Previously-entered words and partially-entered words (e.g., one or morecharacters of a prefix of a new word) can be considered observed contextthat can be used to make predictions. FIG. 3 illustrates exemplaryobserved context for predicting words using a word n-gram model 320 anda unigram model 322 as discussed herein. In one example, the observedcontext can include observed words 324 (which can be made up of previouscharacters 330), space 326, and new prefix character 332, some or all ofwhich can correspond to the typed input received from a user at block202 of process 200. New prefix character 332 following space 326 cancorrespond to a prefix character of a new word being typed by a user.

For reference, and as noted in FIG. 3, predicted word w_(q) (noted aspredicted word 328) can correspond to one of a set of predicted wordsdetermined from word n-gram language model 320 at time 336. As discussedin further detail below, predicted word 328 can be determined based onobserved words 324, noted as w_(q−n+1) . . . w_(q−1), where n can be thenumber of words considered in the word n-gram language model 320.Observed words 324 can be part of the linguistic context c available ata particular time for predicting words. Likewise, predicted word w(noted as predicted word 334) can correspond to one of a set ofpredicted words determined from unigram model 322 at time 336, wherepredicted word 334 can begin with new prefix character 332 (ch₁). In oneexample, new prefix character 332 may have been entered immediatelybefore time 336, and predicted word 334 can be determined at time 336based on new prefix character 332 as discussed in further detail below.Also as discussed in further detail below, at a later time 338, aftertwo additional prefix characters (ch₂ch₃) have been entered, includingnew prefix character 340, unigram model 322 can be used to determinepredicted word 342 beginning with the three-character prefix ch₁ ch₂ch₃.

Referring again to process 200 of FIG. 2, at block 204, a firstprobability of a predicted word can be determined based on a previouslyentered word using a word n-gram model (e.g., word n-gram language model110 of FIG. 1, and as illustrated by word language model 320 of FIG. 3).As understood by one of ordinary skill in the art, a word n-gram modelcan compute the probability (or language score) of a current word w_(q)given the available word history or linguistic context c available at aparticular time, which can include observed words 324 in FIG. 3 (e.g., asequence of previously entered words). This language score can berepresented by the probability equation P(w_(q)|c), which can, asunderstood by one of skill in the art, be expanded, for example, usingBayes' rule as desired for a particular application. This probabilitycan signify a likelihood that a candidate word (e.g., predicted word 328of FIG. 3) corresponds to a word a user will enter (or a word a userwill complete in the instance where one or more characters has alreadybeen entered). One of ordinary skill in the art will understand that thevalue of n can be varied as desired, and a word n-gram model can computea probability of a predicted word given any amount of available wordhistory (e.g., zero words, one word, two words, etc.).

Referring again to process 200 of FIG. 2, at block 206, a secondprobability of the predicted word can be determined based on a prefixcharacter of a new word using a unigram model (e.g., unigram model 112of FIG. 1, and as illustrated by unigram model 322 of FIG. 3). Asunderstood by one of ordinary skill in the art, a unigram model cancompute the probability (or word score) of a current word w in, forexample, a particular language, domain, or the like. This word score canbe represented by the probability P(w). In some examples, a unigrammodel can give character-by-character marginal probabilities of allwords in the current language. Moreover, a unigram model can be filteredbased on a prefix of one or more characters to provide relevantprobabilities corresponding to words having a matching prefix (e.g., newprefix character 332 ch₁, character string ch₁ ch₂ch₃, etc.). Inaddition, as discussed below with reference to FIG. 4, in some examplesthe geometry of key selection on, for example, a virtual keyboard can betaken into account in computing the unigram probability to arrive at ageometry score of a predicted word. One of ordinary skill in the artwill understand that the unigram model can be trained on relevant datasets (e.g., text relevant to a particular domain), structured in avariety of different ways (e.g., in a traversable trie), and modifiedfor particular applications as desired.

Referring again to process 200 of FIG. 2, at block 208, a combinedprobability of the predicted word can be determined based on the firstprobability from the word n-gram model at block 204 and the secondprobability from the unigram model at block 206. In one example, theproduct of the first and second probabilities for the same predictedword can be determined, such as by calculating P(w)·P(w_(q)|c), where wand w_(q) can be the same predicted word used in the unigram model andn-gram model, respectively. In another example, one or both of the firstand second probabilities can be weighted or scored by a factor, modifiedby a logarithm, raised to a power (e.g., an empirically-derived value),or mathematically altered in other ways based, for example, on empiricaltesting to provide accurate and meaningful suggestions in a particularapplication, and to give appropriate weight to the sources ofinformation. For example, one of the probabilities (e.g., the n-grammodel probability) can be raised to a particular value to allow foradjustments in the relative importance of the two models. In still otherexamples, the first and second probabilities can be combined usinglinear interpolation, a joint probability, or the like. Regardless ofhow they are combined for a particular application, as mentioned above,this combination approach can take advantage of the benefits of both aword n-gram model probability P(w_(q)|c) accounting for linguisticcontext and a unigram model probability P(w) accounting for wordlikelihood given prefix characters to obtain accurate word predictionsfrom the available information.

It should be appreciated that the probability calculations discussedabove can be determined for sets of predicted words. For example, forcomputational efficiency and manageability, probabilities can bedetermined for a set number of candidates (e.g., twenty of the likeliestpredicted words, fifty of the likeliest predicted words, one hundred ofthe likeliest predicted words, etc.) as opposed to an unbounded set.Predicted words for the set can be derived from the n-gram model, theunigram model, or a combination of both models, and the combinedprobabilities can be determined for each predicted word regardless ofthe source. In one example, predicted words originating from the unigrammodel can include the associated probability from that model, and theword n-gram model can be queried to acquire the corresponding n-grammodel probability for the word. In another example, predicted wordsoriginating from the n-gram model can include the associated probabilityfrom that model, and the unigram model can be queried to acquire thecorresponding unigram model probability for the word (which, asdiscussed below, can include a geometry score). As discussed below, thepredicted word candidates in the set can be ranked according toprobability.

In one example, a set of predicted words and associated probabilitiesP(w_(q)|c) can be determined using the word n-gram model and availablecontext. Referring to FIG. 3, however, word n-gram language model 320can, in some examples, produce predicted words 328 having prefixcharacters that do not match new prefix character 332. In particular, aword n-gram model can produce predicted words based on linguisticcontext (e.g., observed words 324) without taking into account newlyentered characters for a new word a user may be entering. A word n-grammodel may not update predicted words as new characters are entered untila complete word is recognized (e.g., by receiving a space 326, period,comma, or other break). This can result in predicted words that may nolonger be likely candidates given the known prefix information of thenew word.

Accordingly, in some examples, a subset of the set of predicted wordsfrom the word n-gram model can be generated by removing words from a setof predicted words based on new prefix character 332 (or multiple prefixcharacters). The subset of predicted words can thus include only thosewords having prefixes that match known character prefix information forthe word a user is entering (e.g., new prefix character 332 ch₁, prefixcharacter string ch₁ch₂ch₃, etc.). The combined probability discussedabove referring to block 208 of process 200 can be calculated for eachword in this subset of predicted words. In particular, the n-gramprobability and the unigram probability for each word in the subset canbe determined and combined as described with reference to block 208.This subset approach can save computation time and improve efficiency byignoring words generated by the n-gram model that may be disqualifiedbased on prefix mismatch. The subset can also continue to be pruned asnew characters are revealed. For example, at time 338, any words in thesubset not having a prefix of ch₁ch₂ch₃ can be removed, and thelikeliest candidates that remain can be used (e.g., can be provided to auser).

Similarly, a particular set of predicted words can be determined basedon the unigram model. For example, referring to FIG. 3, at time 336,unigram model 322 can produce a set of predicted words 334 andassociated probabilities P(w). As with the words from the n-gram model,not all predicted words may match the known prefix characters of a newlyentered word (e.g., new prefix character 332). Accordingly, the unigrammodel can be used to determine probabilities P(w) for a set of onlythose words having prefixes that match known character prefixinformation for the word a user is entering (e.g., new prefix character332 ch₁, prefix character string ch₁ch₂ch₃, etc.). In other words,rather than determining probabilities for an unbounded set of words, theunigram model can be used to determine probabilities for a selected setof words with prefixes matching the characters entered for a new word.The combined probability discussed above referring to block 208 ofprocess 200 can be calculated for each word in this set of predictedwords. In particular, the n-gram probability and the unigram probabilityfor each word in the set can be determined and combined as describedwith reference to block 208. The set can also continue to be pruned asnew characters are revealed. For example, at time 338, any words in theset not having a prefix of ch₁ch₂ch₃ can be removed, and the likeliestcandidates that remain can be used (e.g., can be provided to a user). Asequence of characters in the typed input associated with a new word canthus be used to limit the probabilities determined for the unigrammodel.

It should be appreciated that a unigram model as discussed herein can beconstructed in a variety of ways. In one example, the unigram model canbe constructed as a letter trie data structure (also called a digitaltree, radix tree, prefix tree, or the like). As understood by one ofordinary skill in the art, candidate words and their associatedprobabilities can be determined by traversing the unigram letter trie.In one example, given a new prefix character 332 of a new word as inFIG. 3, the trie can be traversed only on those branches emanating fromthe new prefix character. Prediction candidates and their associatedprobabilities can thus be determined for only those words qualifying ascandidates based on their matching prefix character. In other examples,given a string of prefix characters (e.g., ch₁ch₂ch₃), the unigramletter trie can be traversed only on those branches emanating from thefinal character in the string of prefix characters. Predictioncandidates and their associated probabilities can thus be determined foronly those words qualifying as candidates based on their matching prefixstring of characters. A trie data structure can thus provide anefficient way to generate unigram candidate words and associatedprobabilities.

As mentioned above, in some examples, a unigram model in any of theexamples herein can incorporate geometry information in determining theprobability of a particular predicted word. Geometry information caninclude position information of key selection on a keyboard (e.g.,keyboard 116 of FIG. 1), such as on a virtual keyboard on a user device,a virtual keyboard on a display, or the like. Key selection can be doneusing a finger, stylus, mouse pointer, or other indicator. In someexamples, key selection can be imprecise. For example, users can makeambiguous key selections (e.g., based on small key size, imprecisetyping, finger size, etc.). FIG. 4 illustrates text entry in text entrybox 458 using an exemplary virtual keyboard 452 shown on display 450 ofuser device 102. In one example, display 450 can form part of atouchscreen, and key selection can be done, for example, using a fingeror a stylus. As illustrated, key selection 454 and key selection 456 canbe imprecise, bordering on more than one input key (e.g., bordering onthe letters p and o, and the letters e, r, s, and d, respectively). Asshown in text entry box 458, the letter p can be selected for displayfrom key selection 454 and the letter e can be selected for display fromkey selection 456 given that these letters correlate the most with thekey selections.

Although some letters can be more probable than others given aparticular key selection (e.g., the letters p and e), the geometry ofthe key selections can be used to consider alternatives. In particular,instead of limiting word prediction to a single letter associated with aparticular key selection, the geometry of the key selection can be usedto predict words. This can allow for automatic correction of imprecisetyping to the most likely candidate words as well as suggestion ofcandidate words that may be highly likely even if they may not match theletters predominantly associated with a particular sequence of keyselections. In addition to accounting for nearby keys, the predictiondiscussed herein can be further broadened to account for inverted typingmistakes, misspelled words, and the like.

In one example, a geometry score can be introduced into the unigramprobability based on how accurately the geometry of a sequence of keyselections matches predicted words. In particular, the likelihood of asequence of characters given a sequence of key selections (e.g.,position indications on a virtual keyboard) can be introduced into theunigram probability as a geometry score. In other examples, the unigramprobability can be determined based on geometry (e.g., typing location)instead of a specific character or characters (e.g., a unigram trie canbe traversed based on geometry/position instead of or in addition toletters). Similarly, in other examples, a geometry cost can be appliedto a unigram probability based on how much the predicted word variesfrom a sequence of key selections (e.g., based on a distance between acharacter sequence of a predicted word and a sequence of keyselections). Various other methods can also be used to incorporategeometry of key selection into a unigram probability.

Using the unigram model, the probability of a predicted word based ongeometry (e.g., incorporating a geometry score) can be denoted P(w|g),where g refers to the typing geometry. This probability can bedetermined according to any of the methods discussed above foraccounting for the geometry of key selection, and can include a measureof how well a hypothesized word fits a sequence of keystrokes on akeyboard. Likewise, this probability can be used in place of any of theunigram probabilities P(w) discussed herein. In particular, using aunigram model, the probability of a word given key selection geometrycan be determined at block 206 of process 200, and that probability canbe used at block 208 in combination with the word n-gram modelprobability from block 204. For example, the combined probability of apredicted word can be determined at block 208 based on the productP(w|g)·P(w_(q)|c), where w and w_(q) can be the same predicted word usedin the unigram model and n-gram model, respectively. In other examples,one or both of the factors can be raised to a power (e.g., lambda) thatcan be modified to a device-wide constant value to adjust the relativepower of the unigram geometry value versus the language model value. Forexample, the language model probability P(w_(q)|c) can be raised to anempirically-derived power before computing the product with the unigramprobability P(w|g).

Referring to FIG. 4, for example, the geometry of key selection 454 andkey selection 456 in sequence can be used in determining a unigramprobability of a predicted word. One predicted word can include“people,” beginning with the characters p and e shown in text entry box458. An n-gram model can be used to determine a first probability forthe word “people” given the context of the previous words “How” and“many.” A unigram model can be used to determine a second probabilityfor the word “people” given the typing geometry of key selection 454 andkey selection 456 (and/or the keyed characters p and e in otherexamples). A combined probability can then be determined using both ofthese probabilities.

Other predicted words are possible, however, given the same keyselection sequence. For example, the word “orders” can be predicted. Ann-gram model can be used to determine a first probability for the word“orders” given the context of the previous words “How” and “many.” Aunigram model can be used to determine a second probability for the word“orders” given the typing geometry of key selection 454 and keyselection 456. In one example, the unigram probability can be higher for“people” than for “orders” given the positions of key selection 454 andkey selection 456 predominantly on the letters p and e, respectively.Nevertheless, in some examples, alternative words derived from theimprecision of key selection geometry (such as “orders”) can be likely,and users can be presented with such alternatives.

In other examples, one or both of the probabilities P(w|g) andP(w_(q)|c) can be weighted or scored by a factor, modified by alogarithm, raised to a power, or mathematically altered in other waysbased, for example, on empirical testing to provide accurate andmeaningful suggestions in a particular application, and to giveappropriate weight to the sources of information. For example, one ofthe probabilities (e.g., the n-gram model probability) can be raised toa particular value to allow for adjustments in the relative importanceof the two models. In still other examples, these probabilities can becombined using linear interpolation, a joint probability, or the like.Regardless of how they are combined for a particular application, thiscombination approach can take advantage of the benefits of both a wordn-gram model probability P(w_(q)|c) accounting for linguistic contextand a unigram model probability P(w|g) accounting for word likelihoodgiven prefix characters and/or geometry to obtain accurate wordpredictions from the available information.

In still other examples, a geometry score can be applied to the combinedunigram and n-gram probability discussed herein or incorporated in otherways. Moreover, instead of or in addition to applying a geometry score,various techniques for auto-correction of virtual keyboard typing can beused to address typing imprecision when predicting words according tothe examples herein.

As noted above, predicted words can be filtered from the word n-gramlanguage model candidate set based on failing to match a prefix of oneor more characters that a user has entered. In some examples, thisfiltering can be done using the unigram language model. In particular,the prefix used to filter words from the set may not be a string ofcharacters, but can instead be a path into a unigram language model withan accompanying geometry hypothesis that can be used to filter wordsfrom the word n-gram language model prediction set. For example, theunigram probability can be determined as follows:P(w|g)=P(w|prefix)P(prefix|g), where P(prefix|g) can represent ageometry cost associated with the geometry hypothesis in the unigrammodel, and P(w|prefix) can represent a probability weighting factorwhere, for example, the log of the probability may be proportional tothe length of the word beyond the typed prefix. For words that predictno characters beyond the typed prefix, P(w|prefix)=1. The weightingfactor can, for example, discourage lengthy word predictions in favor ofshorter word predictions as desired in certain applications. In thismanner, words can be removed from the n-gram language model set using apath into the unigram language model with accompanying geometryhypothesis information.

In any of the various examples discussed above, the combination of aword n-gram language model probability and a unigram language modelprobability (which can include a geometry score) can provide a robustprediction mechanism. Each model can lean on the other in order togenerate a complete score for a predicted word (e.g., P(w|g)·P(w_(q)|c),where, in some examples, one or both of the factors can be raised to anempirically-derived power before computing the product). Wordsoriginating from the unigram model can naturally include an associatedprobability, which can include a geometry score as in P(w|g), and theother factor P(w_(q)|c) can be obtained by querying the n-gram languagemodel. Words originating from the n-gram language model can naturallyinclude an associated probability P(w_(q)|c), and, in some examples, theother factor P(w|g) can be obtained by determining the probability ofthe geometry hypothesis used to filter the predicted word and adding anadditional cost P(w|prefix) of predicted keys (if called for). Variousmodifications to this combined approach can be made as discussed aboveand based on particular applications.

Referring again to process 200 of FIG. 2, at block 210, the predictedword can be displayed based on the combined probability determined atblock 208 (e.g., based on the combined probability P(w)·P(w_(q)|c), thecombined probability P(w|g)˜P(w_(q)|c), or another combination, where,in some examples, one or both of the factors can be raised to anempirically-derived power before computing the product). In one example,a set of predicted words and their associated probabilities can beranked, and the likeliest candidate or candidates can be displayed. Insome examples, the likeliest word can be displayed in-line where theuser is typing (e.g., in text entry box 458) as an automatic correctionof a word being typed. For example, if the probability of a predictedword is sufficiently high (e.g., exceeds a threshold), existingdisplayed characters can be replaced with the predicted word as anautomatic correction. In other examples, the likeliest word or words(e.g., the top three words) can be displayed as candidate wordsuggestions that a user can select. In such examples, a selectedcandidate word can be entered as text input in its entirety without theuser having to manually enter each of the characters making up the word.New characters from the selected word can be added to existingcharacters (e.g., adding “ople” to “pe” in text entry box 458 forselected word “people”), or a selected word can replace existingcharacters (e.g., removing “pe” and replacing it with selected word“orders”). Predicted words and their associated probabilities determinedaccording to the examples discussed herein can also be displayed and/orused in a variety of other ways.

If no predicted word probability is sufficiently high to replace text asan automatic correction, and if no displayed candidate word is selected,a user may continue typing, either terminating a word (e.g., with aspace, period, comma, or the like) or typing additional characters forthe same word. In the latter case, predicted words can be furtherfiltered based on newly revealed characters in the prefix of the newword. For example, referring again to FIG. 3, at time 336 a single newprefix character 332 ch₁ was revealed, but by time 338, three charactersch₁ch₂ch₃ may have been revealed. Accordingly, at time 338, the set ofpredicted words can be pruned to exclude words not having the fullmatching prefix ch₁ch₂ch₃, and the likeliest candidates remaining thatinclude the matching prefix can be used for automatic correction and/ordisplayed as candidate suggestions. This process can continue repeatedlyas needed to continue providing corrections or meaningful suggestions tothe user.

In some examples, at the beginning of a word, or after a non-wordcharacter (e.g., a space, comma, period, or the like), only the n-grammodel may be queried to suggest likely candidates. After the firstcharacter of the new word is entered, however, the unigram model can bequeried and the probabilities of both models can be combined accordingto the various examples discussed herein. Various other modificationscan similarly be made for the beginning of sentences (e.g., extra tokenweighting), beginning of clauses, words following certain punctuation,and the like.

In addition, in some examples, retroactive correction can be used toretroactively correct previous words in the typed input even after abreaking character (e.g., space, comma, period, etc.). For example, theprobability of a phrase can be determined, and if it is sufficientlyhigh, both a current word and a previous word can be automaticallycorrected to the likely phrase. This can be done by maintainingcorrection hypotheses in memory, and the combined hypotheses of aprevious word and a current word can, in some examples, be sufficientlyhigh so as to justify automatically correcting both the current word anda previous word (or words). An example of retroactive correction isdescribed in Applicants' U.S. Utility application Ser. No. 13/604,439for “Multi-Word Autocorrection,” filed Sep. 5, 2012, the entiredisclosure of which is incorporated herein by reference.

It should be appreciated that process 200 of FIG. 2 and the modeldescriptions and geometry data of FIG. 3 and FIG. 4 are illustrativeexamples, and various modifications will be apparent to those ofordinary skill in the art. For example, it should be understood that,although separated out into three different blocks in FIG. 2, blocks204, 206, and 208 can be combined into a single function. Various othermodifications are also possible.

In any of the various examples discussed herein, language models can bepersonalized for a particular user. For example, word n-gram languagemodels and unigram models discussed herein can be trained onuser-specific information or modified according to user preferences,contacts, text, usage history, profile data, demographics, or the like.In addition, such models can be updated over time based on userinteractions (e.g., frequently entered text or the like). Gathering anduse of user data that is available from various sources can be used toimprove the delivery to users of invitational content or any othercontent that may be of interest to them. The present disclosurecontemplates that in some instances, this gathered data can includepersonal information data that uniquely identifies or can be used tocontact or locate a specific person. Such personal information data caninclude demographic data, location-based data, telephone numbers, emailaddresses, home addresses, or any other identifying information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used todeliver targeted content that is of greater interest to the user.Accordingly, use of such personal information data enables calculatedcontrol of the delivered content. Further, other uses for personalinformation data that benefit the user are also contemplated by thepresent disclosure.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

Despite the foregoing, the present disclosure also contemplates examplesin which users selectively block the use of, or access to, personalinformation data. That is, the present disclosure contemplates thathardware and/or software elements can be provided to prevent or blockaccess to such personal information data. For example, in the case ofadvertisement delivery services, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data duringregistration for services. In another example, users can select not toprovide location information for targeted content delivery services. Inyet another example, users can select to not provide precise locationinformation, but permit the transfer of location zone information.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedexamples, the present disclosure also contemplates that the variousexamples can also be implemented without the need for accessing suchpersonal information data. That is, the various examples of the presenttechnology are not rendered inoperable due to the lack of all or aportion of such personal information data. For example, content can beselected and delivered to users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the content being requested by the deviceassociated with a user, other non-personal information available to thecontent delivery services, or publicly available information.

In accordance with some examples, FIG. 5 shows a functional blockdiagram of an electronic device 500 configured in accordance with theprinciples of the various described examples. The functional blocks ofthe device can be implemented by hardware, software, or a combination ofhardware and software to carry out the principles of the variousdescribed examples. It is understood by persons of skill in the art thatthe functional blocks described in FIG. 5 can be combined or separatedinto sub-blocks to implement the principles of the various describedexamples. Therefore, the description herein optionally supports anypossible combination or separation or further definition of thefunctional blocks described herein.

As shown in FIG. 5, electronic device 500 can include a display unit 502configured to display a text entry interface, and a typed inputreceiving unit 504 configured to receive typed input from a user. Insome examples, typed input receiving unit 504 can be integrated withdisplay unit 502 (e.g., as in a touchscreen). Electronic device 500 canfurther include a processing unit 506 coupled to display unit 502 andtyped input receiving unit 504. In some examples, processing unit 506can include an n-gram probability determining unit 508, a unigramprobability determining unit 510, and a combined probability determiningunit 512.

Processing unit 506 can be configured to receive typed input from a user(e.g., from typed input receiving unit 504). The typed input cancomprise a character associated with a new word. Processing unit 506 canbe further configured to determine (e.g., using a word n-gram model ofn-gram probability determining unit 508) a first probability of apredicted word based on a previously entered word in the typed input.Processing unit 506 can be further configured to determining (e.g.,using a unigram model of unigram probability determining unit 510) asecond probability of the predicted word based on the characterassociated with the new word in the typed input. Processing unit 506 canbe further configured to determine (e.g., using combined probabilitydetermining unit 512) a combined probability of the predicted word basedon the first probability and the second probability. Processing unit 506can be further configured to cause the predicted word to be displayed(e.g., using display unit 502) based on the combined probability.

In some examples, determining the first probability (e.g., using n-gramprobability determining unit 508) comprises determining a set ofpredicted words and associated probabilities based on the previouslyentered word in the typed input. Processing unit 506 can be furtherconfigured to generate a subset of the set of predicted words byremoving words from the set based on the character associated with thenew word in the typed input. In some examples, the subset of predictedwords comprises words having prefixes that comprise the characterassociated with the new word in the typed input, and the subset ofpredicted words comprises the predicted word.

In some examples, the second probability (e.g., from unigram probabilitydetermining unit 510) is determined based on a geometry score associatedwith the typed input. In one example, the geometry score is determinedbased on a key selection. In another example, the geometry scorecomprises a likelihood of a sequence of characters given the keyselection. In still another example, the key selection comprises keyselection on a virtual keyboard.

In some examples, determining the second probability (e.g., usingunigram probability determining unit 510) comprises determining a set ofpredicted words and associated probabilities based on the characterassociated with the new word in the typed input. In one example, the setof predicted words comprises words having prefixes that comprise thecharacter associated with the new word in the typed input, and the setof predicted words comprises the predicted word. In other examples,determining the second probability (e.g., using unigram probabilitydetermining unit 510) comprises traversing a unigram trie to determinethe second probability.

In some examples, determining the first probability (e.g., using n-gramprobability determining unit 508) comprises determining the firstprobability based on a sequence of previously entered words. In otherexamples, determining the second probability (e.g., using unigramprobability determining unit 510) comprises determining the secondprobability based on a sequence of characters in the typed inputassociated with the new word. In still other examples, determining thecombined probability (e.g., using combined probability determining unit512) comprises determining the product of the first probability and thesecond probability.

Although examples have been fully described with reference to theaccompanying drawings, it is to be noted that various changes andmodifications will become apparent to those skilled in the art (e.g.,modifying any of the systems or processes discussed herein according tothe concepts described in relation to any other system or processdiscussed herein). Such changes and modifications are to be understoodas being included within the scope of the various examples as defined bythe appended claims.

What is claimed is:
 1. A method for predicting words, the methodcomprising: at an electronic device: receiving typed input from a user,wherein the typed input comprises a character associated with a newword; determining, using a word n-gram model, a first probability of apredicted word based on a previously entered word in the typed input;determining, using a unigram model, a second probability of thepredicted word based on the character associated with the new word inthe typed input, wherein the second probability is determined based on ageometry score associated with the typed input; determining a combinedprobability of the predicted word based on the first probability and thesecond probability; and causing the predicted word to be displayed basedon the combined probability.
 2. The method of claim 1, whereindetermining the first probability comprises: determining a set ofpredicted words and associated probabilities based on the previouslyentered word in the typed input.
 3. The method of claim 2, furthercomprising: generating a subset of the set of predicted words byremoving words from the set based on the character associated with thenew word in the typed input.
 4. The method of claim 3, wherein thesubset of predicted words comprises words having prefixes that comprisethe character associated with the new word in the typed input; andwherein the subset of predicted words comprises the predicted word. 5.The method of claim 1, wherein the geometry score is determined based ona key selection.
 6. The method of claim 5, wherein the geometry scorecomprises a likelihood of a sequence of characters given the keyselection.
 7. The method of claim 5, wherein the key selection compriseskey selection on a virtual keyboard.
 8. The method of claim 1, whereindetermining the second probability comprises: determining a set ofpredicted words and associated probabilities based on the characterassociated with the new word in the typed input.
 9. The method of claim8, wherein the set of predicted words comprises words having prefixesthat comprise the character associated with the new word in the typedinput; and wherein the set of predicted words comprises the predictedword.
 10. The method of claim 1, wherein determining the secondprobability comprises: traversing a unigram trie to determine the secondprobability.
 11. The method of claim 1, wherein determining the firstprobability comprises: determining the first probability based on asequence of previously entered words.
 12. The method of claim 1, whereindetermining the second probability comprises: determining the secondprobability based on a sequence of characters in the typed inputassociated with the new word.
 13. The method of claim 1, whereindetermining the combined probability comprises: determining the productof the first probability and the second probability.
 14. The method ofclaim 1, wherein the electronic device comprises a phone, a desktopcomputer, a laptop computer, a tablet computer, a television, atelevision set top box, or a wearable electronic device.
 15. Anon-transitory computer-readable storage medium comprising instructionsfor causing one or more processors to: receive typed input from a user,wherein the typed input comprises a character associated with a newword; determine, using a word n-gram model, a first probability of apredicted word based on a previously entered word in the typed input;determine, using a unigram model, a second probability of the predictedword based on the character associated with the new word in the typedinput, wherein the second probability is determined based on a geometryscore associated with the typed input; determine a combined probabilityof the predicted word based on the first probability and the secondprobability; and cause the predicted word to be displayed based on thecombined probability.
 16. A system comprising: one or more processors;memory; one or more programs, wherein the one or more programs arestored in the memory and configured to be executed by the one or moreprocessors, the one or more programs including instructions for:receiving typed input from a user, wherein the typed input comprises acharacter associated with a new word; determining, using a word n-grammodel, a first probability of a predicted word based on a previouslyentered word in the typed input; determining, using a unigram model, asecond probability of the predicted word based on the characterassociated with the new word in the typed input, wherein the secondprobability is determined based on a geometry score associated with thetyped input; determining a combined probability of the predicted wordbased on the first probability and the second probability; and causingthe predicted word to be displayed based on the combined probability.17. The computer-readable storage medium of claim 15, whereindetermining the first probability comprises: determining a set ofpredicted words and associated probabilities based on the previouslyentered word in the typed input.
 18. The computer-readable storagemedium of claim 17, further comprising instructions for causing the oneor more processors to: generate a subset of the set of predicted wordsby removing words from the set based on the character associated withthe new word in the typed input.
 19. The system of claim 16, whereindetermining the first probability comprises: determining a set ofpredicted words and associated probabilities based on the previouslyentered word in the typed input.
 20. The system of claim 19, wherein theone or more programs further include instructions for: generating asubset of the set of predicted words by removing words from the setbased on the character associated with the new word in the typed input.